Did you know we published our first template in the @n8n_io template directory? Now, you can build RAG workflows in n8n using Pinecone Assistant. Using the built-in Pinecone Vector Store node gives you flexibility if you need to control all aspects of chunking, embedding, and

High throughput, billion-scale, and constant updates? AI apps have conflicting demands. You can't trade freshness for scale. We built our "slab architecture" to deliver both. See how we abstract the complexity so you can build. Read more: https://t.co/aruhMOL1BI

October updates are live 🚀 → Million-scale namespaces  → CLI v0.1.0 for terminal workflows → Bulk update/delete/fetch by metadata  → 100 files per assistant on Starter  → AI engine integration guides Read more: https://t.co/G1I2dlgkwy

See docs:

Pinecone has added two significant upgrades to the Standard plan: 1. Single Sign-On (SSO) is now included for all users, simplifying access. 2. Namespace limit increased to 100K per index, up from 25K, enhancing scalability. These changes improve operational flexibility and

We just launched bulk data operations by metadata in Pinecone → Update, delete, fetch records using filters  → No ID collection required  → Same syntax as queries  → Works across millions of records Docs: https://t.co/WPnPFdMycs

10,000+ customers. 400M+ vectors. <20ms latency. #1 in RAG accuracy. @CustomGPT scales domain-specific GPTs with Pinecone. 🔗 https://t.co/OylIV12WZm

💡 The real question isn’t “build or buy.” It’s: What’s the cost of building? AI teams rarely fail at prototyping. They fail at scaling. @Aquant_ai’s story is a lesson: ⏰ 🐌 🤕 Building your own vector DB = 1 FTE, slow roadmap, brittle infra. ⚡Buying = time-to-value in days.

New: Build Pinecone apps with AI agents instead of copy-pasting code 🚀 Use Claude Code or Cursor to implement search, RAG, and recommendations with best practices built in. Try it → https://t.co/SbplclGv5s

See documentation:

Importing data from Google Cloud Storage to Pinecone significantly reduces overhead in data ingestion. This method allows for streamlined development and faster iterations with production-scale data. Expect cost savings of up to 6x compared to traditional methods. Efficient and

Join us on Oct 30 for the n8n Community Livestream. n8n Product Manager David Arens shares early designs for Guardrails and Human-in-the-Loop nodes, the @pinecone team demos their new node, and Marrallisa Kreijkes, Amsterdam Ambassador, talks about her n8n coworking sessions

Read the blog 🔗

Make knowledge hidden in your Google Docs discoverable and actionable. This post by John Ward, a Solutions Engineer here at Pinecone, shows how to load Google Docs into Pinecone Assistant, then ask natural-language questions and quickly surface answers across your notes, PRDs,

Learn more 🔗

ICYMI: Pinecone offers a 3-week free trial of its Standard plan, providing $300 in credits and full access to features. This enables testing at higher limits for data projects.

RAG is evolving from basic implementations to complex systems. It's essential for grounding decisions in real-time data and optimizing training for specialized models. It allows fresh data access without the costs of retraining. As agentic systems grow, RAG becomes a key player

Open source isn’t free. “We used to build our own vector DB. But you end up with an elephant you’re stuck with for years.” – Oded Sagie, VP R&D, @Aquant_ai The hidden cost of building AI infra yourself: 👩‍💻 $200K+/yr in FTEs ⏰ Months to go live 🐌 Slower product velocity 🤕

🔍 Diving into the world of Context Engineering! Join us on Oct 23 for an exclusive Chicago QA event hosted by Forever22, Pinecone, and TeamWorking by TechNexus featuring expert insights from our own Arjun Patel. See live demos, network with fellow AI builders, and unlock

@halfabrane @DevInterrupted 📺 https://t.co/H3FGo5CwlL 👂